Zero Downtime Update Implementation Program
The dynamic update mechanism provided by KBLaM consists of three key components:
- Versioning Knowledge Management: Adoption
git-lfsmanagerial.npyVector file, generated for each updateembeddings_v[timestamp].npy - Thermal loading technology: By
integrate.py(used form a nominal expression)--hotloadParameters enable model memory switching without restarting API services - AB Test Mode: Use
experiments/ab_testing.pyScript comparing the effect of old and new knowledge versions
Recommended operating procedures: 1) process knowledge updates in the new branch; 2) use thegenerate_kb_embeddings.py --deltaOnly new content vectors are calculated; 3) Validation and deployment are automated through the CI/CD pipeline. Microsoft's internal practice has shown that this approach can shorten the knowledge update cycle from hourly to minute level and reduce the error rate by 70%.
This answer comes from the articleKBLaM: An Open Source Enhanced Tool for Embedding External Knowledge in Large ModelsThe
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